Granger Causality and Regression

I have an enquiry regarding the Granger Causality analysis. It is said that it is performed to check whether “X causes Y”, or to put it differently, whether X contains any predictive information with regards to Y and it mainly builds two regression models (one nested to other).

The first model (unrestricted) regresses Y against lagged values of Y and lagged values of X while the second model (restricted) regresses Y against lagged values of Y. It then uses a nested F test to compare the two models and draw conclusions.

Thus I am wondering, what’s the difference between this and a time series linear regression model which uses lagged values of the same variables for making predictions. In case a significant predictor is found, can one use the respective unrestricted model for prediction purposes?

Furthermore, can the Granger Causality analysis be automatically performed by any of the following software packages? (SAS, SPSS, Minitab or Excel) I need to examine the significance (nested F tests) for several potential predictors individually – for varying time lags too obviously – so I would like to know whether there is an easy way to do so instead of having to manually build up the tests.